The use of an educational video on forensic autopsy in curricular teaching for medical students-is it worth the effort? : A field report from lecturers and a survey among students.

PURPOSE: The purpose of this work is to share our experience with an educational video on forensic autopsy. Using questionnaires, we attempted to answer the following questions: Does watching the video trigger emotions in students? Does the autopsy meet the expectations that they had before? Does the video help to prepare them for their subsequent autopsy participation? METHODS: A total of 365 medical students who attended their classes during the COVID-19 pandemic measures were provided with the video on an online platform. Links leading to questionnaires were positioned before and after the video. One hundred seventy-six students returned to face-to-face teaching during their course in forensic medicine. Those among them who chose to participate in an autopsy at our institute were given the link to a third questionnaire after their autopsy participation. The data was analyzed using IBM SPSS 27.0 and Microsoft Excel. RESULTS: One hundred seventy-two students completed a questionnaire before watching the educational video, 85 also completed one afterwards, and 28 completed the third questionnaire. The most intense feelings while watching the video were "curiosity" and "surprise". Out of twelve students (14.1%) who had imagined the autopsy differently in advance, five perceived the autopsy shown in the video as rougher or more brutal than expected. All autopsy participants who had previously viewed the video felt adequately prepared. CONCLUSION: Teaching should include an introduction to the handling of the corpse and the general procedures in the dissecting room. Although a video cannot substitute for personal interaction, it is useful to prepare students for their autopsy participation.

Distinct pulmonary patterns in ANCA-associated vasculitides: insights from a retrospective single center cohort study.

ANCA-associated vasculitides (AAV) comprise granulomatosis with polyangiitis (GPA), microscopic polyangiitis (MPA) and eosinophilic granulomatosis with polyangiitis. All forms may involve different organ systems, yet kidney and lung involvement are common and fatal in many cases. Here, we aimed to determine the predictive value of pulmonary disease manifestation and individual CT findings in AAV patients. Available CT scans and clinical information on mortality, renal outcomes, occurrence of relapses and damage scores were analysed retrospectively from a tertiary rheumatology center in Germany. We included a total of 94 AAV patients (49 with GPA, 41 with MPA). Forty-four patients had lung involvement with available CT scans, 70.5% of which with GPA and 72.7% with renal involvement. Nodule formation and cavities were more frequent among GPA patients, whereas ground-glass opacities (GGO), ILD and pleural effusion were observed predominantly in MPA patients. Over a median follow-up of 37 months, GPA patients had a slightly higher overall mortality, whereas end-stage kidney failure rates were significantly increased in MPA patients. Relapse frequencies were comparable between both entities. The presence of GGO and pleural effusion were associated with higher relapse rates, whereas nodules were negatively correlated with relapses. Notably, RTX-treated patients had less infections as compared to individuals under different therapies. Our data demonstrate the outstanding importance of characteristic CT patterns in AAV diagnosis assessment. Especially certain CT patterns including GGO and pleura effusion may help to identify patients who are at higher risk for relapsing disease.

[Vasculitides]. / Vaskulitiden.

In everyday clinical practice, immunologically mediated systemic vasculitides are among the rare diseases, meaning that basic knowledge of major symptoms and indicative laboratory findings is crucial for the inclusion of these complex clinical entities in differential diagnostic considerations. For many years, systemic vasculitides have been classified according to the primarily affected vessel size, distinguishing large, medium-sized, and small vessels. Pain is very often one of the main complaints of these diseases, be it, for example, the temporally accentuated headache in giant cell arteritis, the early morning myalgias in the shoulder and hip girdle in polymyalgia rheumatica, or the mononeuritis multiplex in eosinophilic granulomatosis with polyangiitis. General symptoms such as fever, weight loss, and night sweats are often accompanied by greatly increased parameters of inflammation. In addition, organ-specific symptoms and/or laboratory abnormalities may provide crucial information. These include ENT symptoms, pulmonary or skin manifestations, as well as signs of renal involvement, such as peripheral edema, rise in blood pressure, hematuria, proteinuria, or a rapid loss of kidney function. If there is reasonable suspicion of disease, patients should be transferred to specialized centers with an interdisciplinary team. In most cases, an immunosuppressive therapy regimen is required, although in recent years the path towards avoiding high glucocorticoid doses with many side effects has been paved by the use of novel therapies.

[Prioritized appointment allocation in new patients, what is really decisive? : Comparative analysis of manual appointment allocation with automated and AI-assisted approaches]. / Priorisierte Terminvergabe bei Neuvorstellungen: Was ist wirklich entscheidend? : Vergleichende Analyse manueller Terminallokation mit automatisierten und KI-gestützten Ansätzen.

BACKGROUND: The timely allocation of appointments for new patients is a daily challenge in rheumatological practice, which can be supported by digital solutions. The question is to find the simplest and most effective possible method for prioritization when allocating appointments. METHODS: Using a registration form for new patients, standardized symptoms and laboratory results were collated. After reviewing this information by a medical specialist the allocation of appointments was carried out in three categories: a) < 6 weeks, b) 6 weeks up to 3 months and c) > 3 months. The waiting time between the time of registration and the presentation appointment was calculated and compared between patients with and without a diagnosis of an inflammatory rheumatic disease (IRD). In addition a decision tree (DT), a method taken from the field of supervised learning within artificial intelligence (AI), was established and the resulting classification was compared with respect to the accuracy and calculated saving in waiting time. RESULTS: In this study 800 appointments between 2020 and 2023 (including 555 women, 69.4%, median age 53 years, interquartile range, IQR 39-63 years) were analyzed. An IRD could be confirmed in 409 (51.1%) cases with a waiting time of 58 vs. 93 days for non-IRD cases (-38%, p < 0.01). An AI-based stratification resulted in an accuracy of 67% for IRD and a predicted saving of 19% waiting time. The accuracy increased up to 78% with a time saving for IRD cases of up to 31%, when all basic laboratory results were known. Simplified algorithms, e.g., stratification by the use of laboratory findings alone, resulted in a lower accuracy and time savings. CONCLUSION: Manual allocation of appointments by a medical specialist is effective and significantly reduces the waiting times for patients with IRD. An automated categorization can lead to a reduction in waiting times for appointments when taking complete laboratory results and a lower sensitivity into consideration.

A fair experimental comparison of neural network architectures for latent representations of multi-omics for drug response prediction.

BACKGROUND: Recent years have seen a surge of novel neural network architectures for the integration of multi-omics data for prediction. Most of the architectures include either encoders alone or encoders and decoders, i.e., autoencoders of various sorts, to transform multi-omics data into latent representations. One important parameter is the depth of integration: the point at which the latent representations are computed or merged, which can be either early, intermediate, or late. The literature on integration methods is growing steadily, however, close to nothing is known about the relative performance of these methods under fair experimental conditions and under consideration of different use cases. RESULTS: We developed a comparison framework that trains and optimizes multi-omics integration methods under equal conditions. We incorporated early integration, PCA and four recently published deep learning methods: MOLI, Super.FELT, OmiEmbed, and MOMA. Further, we devised a novel method, Omics Stacking, that combines the advantages of intermediate and late integration. Experiments were conducted on a public drug response data set with multiple omics data (somatic point mutations, somatic copy number profiles and gene expression profiles) that was obtained from cell lines, patient-derived xenografts, and patient samples. Our experiments confirmed that early integration has the lowest predictive performance. Overall, architectures that integrate triplet loss achieved the best results. Statistical differences can, overall, rarely be observed, however, in terms of the average ranks of methods, Super.FELT is consistently performing best in a cross-validation setting and Omics Stacking best in an external test set setting. CONCLUSIONS: We recommend researchers to follow fair comparison protocols, as suggested in the paper. When faced with a new data set, Super.FELT is a good option in the cross-validation setting as well as Omics Stacking in the external test set setting. Statistical significances are hardly observable, despite trends in the algorithms' rankings. Future work on refined methods for transfer learning tailored for this domain may improve the situation for external test sets. The source code of all experiments is available under https://github.com/kramerlab/Multi-Omics_analysis.

Imaging patterns of cerebral ischemia in hypereosinophilic syndrome: case report and systematic review.

INTRODUCTION: Ischemic stroke is a potential complication of hypereosinophilic syndromes (HES), and little is known about underlying pathophysiological mechanisms. We aimed to describe the imaging patterns of cerebral ischemia in patients with HES. METHODS: An individual case is reported. A systematic PubMed review of all records reporting adult patients with HES who suffered ischemic stroke and for whom neuroimaging details of ischemic lesions were available was performed. RESULTS: A 60-year-old man presented with progressive subacute gait difficulty and psychomotor slowing as well as an absolute eosinophilia (2.2 × 109/L) at admission. Brain magnetic resonance tomography revealed multiple acute and subacute internal and external border zone infarcts. Cardiac diagnostic suggested the presence of endomyocarditis. After extensive diagnostic workup, idiopathic HES was diagnosed. The systematic review yielded 183 studies, of which 40 fulfilled the inclusion criteria: a total of 64 patients (31.3% female), with mean age 51.1 years and a median absolute eosinophile count at diagnosis of 10.2 × 109/L were included in the analyses. A border zone pattern of cerebral ischemic lesions was reported in 41 patients (64.1%). Isolated peripheral infarcts were reported in 7 patients (10.9%). Sixteen patients had multiple acute infarcts with no border zone distribution (25.0%). An intracardiac thrombus was reported in 15/60 patients (25%), and findings suggestive of endomyocarditis or endomyocardial fibrosis were found in 31/60 patients (51.7%). CONCLUSIONS: Border zone distribution of cerebral ischemia without hemodynamic compromise is the most frequent imaging pattern in patients with HES, occurring in 2/3 of patients who develop ischemic stroke.

The dignity of the human corpse in forensic medicine.

Working with the dead is a very specific kind of work. Although a dignified handling of the corpses is demanded by the legislator and by the general public, neither the legal status of the corpse is undisputed nor is it obvious what a dignified handling of the deceased should consist of. In our hypothesis generating pilot study, we asked which concrete considerations are involved in daily practice of forensic specialists. We used an online questionnaire (invitations via e-mail) consisting of questions with single choice, multiple choice, and free text entries. The answers to single or multiple choice questions were displayed in pivot tables. The data was thus summarized, viewed, descriptively analyzed, and displayed together with the free text answers. 84.54% of the physicians and 100% of the autopsy assistants stated that considerations concerning the dignity of the deceased should play a role in daily autopsy practice. 45.87% stated that the conditions surrounding the autopsy need improvement to be ethically suitable. The analysis of the survey's results was based on Robert Audi's ethics, according to which three aspects need to be lightened in order to evaluate the conduct of a person morally: the actions, the motivation, and the way in which the actions are carried out. This systematization helps to identify the need for improvement and to make the vague demands for a dignified handling of corpses more concrete.

Polymeric Nanoparticles with Neglectable Protein Corona.

The current understanding of nanoparticle-protein interactions indicates that they rapidly adsorb proteins upon introduction into a living organism. The formed protein corona determines thereafter identity and fate of nanoparticles in the body. The present study evaluates the protein affinity of three core-crosslinked polymeric nanoparticles with long circulation times, differing in the hydrophilic polymer material forming the particle surface, namely poly(N-2-hydroxypropylmethacrylamide) (pHPMA), polysarcosine (pSar), and poly(ethylene glycol) (PEG). This includes the nanotherapeutic CPC634, which is currently in clinical phase II evaluation. To investigate possible protein corona formation, the nanoparticles are incubated in human blood plasma and separated by asymmetrical flow field-flow fractionation (AF4). Notably, light scattering shows no detectable differences in particle size or polydispersity upon incubation with plasma for all nanoparticles, while in gel electrophoresis, minor amounts of proteins can be detected in the particle fraction. Label-free quantitative proteomics is additionally applied to analyze and quantify the composition of the proteins. It proves that some proteins are enriched, but their concentration is significantly less than one protein per particle. Thus, most of the nanoparticles are not associated with any proteins. Therefore, this work underlines that polymeric nanoparticles can be synthesized, for which a protein corona formation does not take place.

[Cerebrovascular events, headache and livedoracemosa - diagnosis at a glance?] / Zerebrovaskuläre Ereignisse, Kopfschmerzen und Livedo racemosa ­ Blickdiagnose?

Sneddon's syndrome is a rare disease characterized by cerebrovascular events and livedo racemosa. There are often autoimmunological comorbidities, especially antiphospholipid antibody syndrome. The underlying pathophysiology is still not fully clarified. A causal therapy does not exist. The reported case shows a patient with a thrombophilic form of Sneddon's syndrome with the main symptoms of headache and thromboembolic events. Symptoms, laboratory parameters, histology and differential diagnoses are explained.

HPMA-Based Nanoparticles for Fast, Bioorthogonal iEDDA Ligation.

Fast and bioorthogonally reacting nanoparticles are attractive tools for biomedical applications such as tumor pretargeting. In this study, we designed an amphiphilic block copolymer system based on HPMA using different strategies to introduce the highly reactive click units 1,2,4,5-tetrazines (Tz) either at the chain end (Tz-CTA) or statistical into the hydrophobic block. This reactive group undergoes a rapid, bioorthogonal inverse electron-demand Diels-Alder reaction (iEDDA) with trans-cyclooctenes (TCO). Subsequently, this polymer platform was used for the preparation of different Tz-covered nanoparticles, such as micelles and colloids. Thereby it was found that the reactivity of the polymeric micelles is comparable to that of the low molar mass tetrazines. The core-cross-linked micelles can be successfully conjugated at rather low concentrations to large biomacromolecules like antibodies, not only in physiological buffer, but also in human blood plasma, which was confirmed by fluorescence correlation spectroscopy (FCS).

enviPath--The environmental contaminant biotransformation pathway resource.

The University of Minnesota Biocatalysis/Biodegradation Database and Pathway Prediction System (UM-BBD/PPS) has been a unique resource covering microbial biotransformation pathways of primarily xenobiotic chemicals for over 15 years. This paper introduces the successor system, enviPath (The Environmental Contaminant Biotransformation Pathway Resource), which is a complete redesign and reimplementation of UM-BBD/PPS. enviPath uses the database from the UM-BBD/PPS as a basis, extends the use of this database, and allows users to include their own data to support multiple use cases. Relative reasoning is supported for the refinement of predictions and to allow its extensions in terms of previously published, but not implemented machine learning models. User access is simplified by providing a REST API that simplifies the inclusion of enviPath into existing workflows. An RDF database is used to enable simple integration with other databases. enviPath is publicly available at https://envipath.org with free and open access to its core data.

Identification of ELF3 as an early transcriptional regulator of human urothelium.

Despite major advances in high-throughput and computational modelling techniques, understanding of the mechanisms regulating tissue specification and differentiation in higher eukaryotes, particularly man, remains limited. Microarray technology has been explored exhaustively in recent years and several standard approaches have been established to analyse the resultant datasets on a genome-wide scale. Gene expression time series offer a valuable opportunity to define temporal hierarchies and gain insight into the regulatory relationships of biological processes. However, unless datasets are exactly synchronous, time points cannot be compared directly. Here we present a data-driven analysis of regulatory elements from a microarray time series that tracked the differentiation of non-immortalised normal human urothelial (NHU) cells grown in culture. The datasets were obtained by harvesting differentiating and control cultures from finite bladder- and ureter-derived NHU cell lines at different time points using two previously validated, independent differentiation-inducing protocols. Due to the asynchronous nature of the data, a novel ranking analysis approach was adopted whereby we compared changes in the amplitude of experiment and control time series to identify common regulatory elements. Our approach offers a simple, fast and effective ranking method for genes that can be applied to other time series. The analysis identified ELF3 as a candidate transcriptional regulator involved in human urothelial cytodifferentiation. Differentiation-associated expression of ELF3 was confirmed in cell culture experiments and by immunohistochemical demonstration in situ. The importance of ELF3 in urothelial differentiation was verified by knockdown in NHU cells, which led to reduced expression of FOXA1 and GRHL3 transcription factors in response to PPARγ activation. The consequences of this were seen in the repressed expression of late/terminal differentiation-associated uroplakin 3a gene expression and in the compromised development and regeneration of urothelial barrier function.

Everolimus in patients with autosomal dominant polycystic kidney disease.

BACKGROUND: Autosomal dominant polycystic kidney disease (ADPKD) is a slowly progressive hereditary disorder that usually leads to end-stage renal disease. Although the underlying gene mutations were identified several years ago, efficacious therapy to curtail cyst growth and prevent renal failure is not available. Experimental and observational studies suggest that the mammalian target of rapamycin (mTOR) pathway plays a critical role in cyst growth. METHODS: In this 2-year, double-blind trial, we randomly assigned 433 patients with ADPKD to receive either placebo or the mTOR inhibitor everolimus. The primary outcome was the change in total kidney volume, as measured on magnetic resonance imaging, at 12 and 24 months. RESULTS: Total kidney volume increased between baseline and 1 year by 102 ml in the everolimus group, versus 157 ml in the placebo group (P=0.02) and between baseline and 2 years by 230 ml and 301 ml, respectively (P=0.06). Cyst volume increased by 76 ml in the everolimus group and 98 ml in the placebo group after 1 year (P=0.27) and by 181 ml and 215 ml, respectively, after 2 years (P=0.28). Parenchymal volume increased by 26 ml in the everolimus group and 62 ml in the placebo group after 1 year (P=0.003) and by 56 ml and 93 ml, respectively, after 2 years (P=0.11). The mean decrement in the estimated glomerular filtration rate after 24 months was 8.9 ml per minute per 1.73 m2 of body-surface area in the everolimus group versus 7.7 ml per minute in the placebo group (P=0.15). Drug-specific adverse events were more common in the everolimus group; the rate of infection was similar in the two groups. CONCLUSIONS: Within the 2-year study period,as compared with placebo, everolimus slowed the increase in total kidney volume of patients with ADPKD but did not slow the progression of renal impairment [corrected]. (Funded by Novartis; EudraCT number, 2006-001485-16; ClinicalTrials.gov number, NCT00414440.)

DySC: software for greedy clustering of 16S rRNA reads.

UNLABELLED: Pyrosequencing technologies are frequently used for sequencing the 16S ribosomal RNA marker gene for profiling microbial communities. Clustering of the produced reads is an important but time-consuming task. We present Dynamic Seed-based Clustering (DySC), a new tool based on the greedy clustering approach that uses a dynamic seeding strategy. Evaluations based on the normalized mutual information (NMI) criterion show that DySC produces higher quality clusters than UCLUST and CD-HIT at a comparable runtime. AVAILABILITY AND IMPLEMENTATION: DySC, implemented in C, is available at http://code.google.com/p/dysc/ under GNU GPL license.

Similarity boosted quantitative structure-activity relationship--a systematic study of enhancing structural descriptors by molecular similarity.

The concept of molecular similarity is one of the most central in the fields of predictive toxicology and quantitative structure-activity relationship (QSAR) research. Many toxicological responses result from a multimechanistic process and, consequently, structural diversity among the active compounds is likely. Combining this knowledge, we introduce similarity boosted QSAR modeling, where we calculate molecular descriptors using similarities with respect to representative reference compounds to aid a statistical learning algorithm in distinguishing between different structural classes. We present three approaches for the selection of reference compounds, one by literature search and two by clustering. Our experimental evaluation on seven publicly available data sets shows that the similarity descriptors used on their own perform quite well compared to structural descriptors. We show that the combination of similarity and structural descriptors enhances the performance and that a simple stacking approach is able to use the complementary information encoded by the different descriptor sets to further improve predictive results. All software necessary for our experiments is available within the cheminformatics software framework AZOrange.

Relapsing TIPIC Syndrome after Administration of an mRNA-Based COVID-19 Vaccine.

Reported vascular complications following mRNA-based COVID-19 vaccines are consisting of myocarditis, cerebral venous thrombosis, cerebral vascular thrombosis, and vaccine-induced thrombocytopenia. Here, we describe a case of a 49-year-old woman with left-sided pain above the middle common carotid artery (carotidynia) starting a few days after her second vaccination with an mRNA-based COVID-19 vaccine (Spikevax). Imaging was indicative of transient perivascular inflammation of the carotid artery (TIPIC) syndrome. The diagnostic workup for other immunologically mediated diseases was negative. The inflammation subsided after a course of prednisone and aspirin, and clinical symptoms vanished, but later mildly relapsed in the context of a viral upper respiratory tract infection other than SARS-CoV-2. Carotidynia because of TIPIC syndrome may present as an immunogenic side effect of the newly developed mRNA-based vaccinations against COVID-19. TIPIC syndrome should be considered in new-onset neck pain after vaccination.

Discriminative machine learning for maximal representative subsampling.

Biased population samples pose a prevalent problem in the social sciences. Therefore, we present two novel methods that are based on positive-unlabeled learning to mitigate bias. Both methods leverage auxiliary information from a representative data set and train machine learning classifiers to determine the sample weights. The first method, named maximum representative subsampling (MRS), uses a classifier to iteratively remove instances, by assigning a sample weight of 0, from the biased data set until it aligns with the representative one. The second method is a variant of MRS - Soft-MRS - that iteratively adapts sample weights instead of removing samples completely. To assess the effectiveness of our approach, we induced artificial bias in a public census data set and examined the corrected estimates. We compare the performance of our methods against existing techniques, evaluating the ability of sample weights created with Soft-MRS or MRS to minimize differences and improve downstream classification tasks. Lastly, we demonstrate the applicability of the proposed methods in a real-world study of resilience research, exploring the influence of resilience on voting behavior. Through our work, we address the issue of bias in social science, amongst others, and provide a versatile methodology for bias reduction based on machine learning. Based on our experiments, we recommend to use MRS for downstream classification tasks and Soft-MRS for downstream tasks where the relative bias of the dependent variable is relevant.

Multiple instance learning to predict immune checkpoint blockade efficacy using neoantigen candidates.

Previous studies showed that the neoantigen candidate load is an imperfect predictor of immune checkpoint blockade (ICB) efficacy. Further studies provided evidence that the response to ICB is also affected by the qualitative properties of a few or even single candidates, limiting the predictive power based on candidate quantity alone. Here, we predict ICB efficacy based on neoantigen candidates and their neoantigen features in the context of the mutation type, using Multiple-Instance Learning via Embedded Instance Selection (MILES). Multiple instance learning is a type of supervised machine learning that classifies labeled bags that are formed by a set of unlabeled instances. MILES performed better compared with neoantigen candidate load alone for low-abundant fusion genes in renal cell carcinoma. Our findings suggest that MILES is an appropriate method to predict the efficacy of ICB therapy based on neoantigen candidates without requiring direct T cell response information.

An ultra-high-throughput screen for the evaluation of peptide HLA-Binder interactions.

Peptide human leukocyte antigen (pHLA) targeting therapeutics like T-cell receptor based adoptive cell therapy or bispecific T cell engaging receptor molecules hold great promise for the treatment of cancer. Comprehensive pre-clinical screening of therapeutic candidates is important to ensure patient safety but is challenging because of the size of the potential off-target space. By combining stabilized peptide-receptive HLA molecules with microarray printing and screening, we have developed an ultra-high-throughput screening platform named ValidaTe that enables large scale evaluation of pHLA-binder interactions. We demonstrate its potential by measuring and analyzing over 30.000 binding curves for a high-affinity T cell Engaging Receptor towards a large pHLA library. Compared to a dataset obtained by conventional bio-layer interferometry measurements, we illustrate that a massively increased throughput (over 650 fold) is obtained by our microarray screening, paving the way for use in pre-clinical safety screening of pHLA-targeting drugs.